CyberTraining: CIU: SJSU Data Science for All Seminar Series

美国的研究企业面临数据科学家短缺的问题。扩大数据科学学生的渠道，特别是在代表性不足的人群中，需要教育机构提高对数据科学的认识，并在学生开始学术生涯时激发他们对数据的热情。目前，很少有社区学院或本科课程向广大学生提供网络基础设施工具或数据科学技术方面的培训。该项目采用了一种新颖的方法，通过培训社区学院和本科生，通过一系列“全民数据科学”课外研讨会，为数据科学家提供数据分析支持，从而增加国家的数据科学劳动力。这些研讨会不需要事先的数据科学知识，强调可转移的技能，并为来自广泛学科和代表性不足群体的学生提供了一条进入数据科学相关研究和其他职业的可行途径，而不会延长他们的时间到毕业。通过提高国家的数据科学能力和数据科学研究队伍的多样性，该项目符合国家利益，正如NSF的使命所述：促进科学进步，促进国家的繁荣和福利。该项目的目标是提高本科生对数据驱动科学的认识，并增加和多样化受过数据整理训练的学生人数——数据采集、转换、清理和分析所需的数据。根据行业专家的说法，数据争论是数据科学的“繁重工作”，占数据科学家日常工作的80%。将这项耗时的工作转移给训练有素的数据分析师，使数据科学家可以将更多的时间集中在研究上。该项目通过开发和交付广泛消费的课外研讨会来实现其目标，这些研讨会为本科生和社区大学生提供数据科学概念和行业领先的数据争论工具的互动培训。最初的研讨会主题是与项目顾问委员会合作选择的，包括Python、Jupyter笔记本、Apache Spark、Tableau和揭开人工智能（AI）的神秘面纱。研讨会对数据争论的关注还向学生介绍了数据准备文档——捕获可重复科学所需的数据来源。该项目通过免费和开放的研讨会材料和补充资源以及在线讲师支持社区，扩大了对国家数据科学劳动力的贡献。为了鼓励旧金山湾区社区学院和大学采用联合教学和教师授课模式来培训教师。该项目通过确定对不同本科生群体的数据科学教学最有效的教学方法，为教学研究做出贡献。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The Nation's research enterprise faces a shortage of data scientists. Expanding the pipeline of data science students, particularly from underrepresented populations, requires educational institutions to increase awareness of data science and inspire a passion for data in students as they begin their academic careers. Currently, few community colleges or undergraduate programs provide training in cyberinfrastructure tools or data science techniques to a broad student population. This project takes a novel approach to augmenting the Nation's data science workforce by training community college and undergraduate students to provide data analytics support to data scientists through a series of "Data Science for All" extracurricular seminars. The seminars require no prior data science knowledge, emphasize transferable skills, and present a feasible path into data science-related research and other careers for students from a broad array of disciplines and from underrepresented groups without extending their time to graduation. By increasing the Nation's data science capabilities and the diversity of its data science research workforce, the project serves the national interest, as stated by NSF's mission: to promote progress of science and advance the prosperity and welfare of the Nation. The goals of this project are to increase undergraduate student awareness of data-driven science and to grow and diversify the population of students trained to perform data wrangling - the data acquisition, transformation, cleaning, and profiling required to prepare data for analysis. According to industry experts, data wrangling is the "heavy lifting" of data science, constituting up to 80% of a data scientist's daily work. Shifting this time-consuming effort to trained data analysts free data scientists to focus more of their time on research. The project achieves its goals through the development and delivery of widely consumable, extracurricular seminars providing interactive training on data science concepts and industry-leading data wrangling tools to undergraduate and community college students. Initial seminar topics, selected in collaboration with the project's advisory board, include Python, Jupyter notebooks, Apache Spark, Tableau, and demystifying artificial intelligence (AI). The seminars' focus on data wrangling also introduces students to data preparation documentation - capturing the data provenance needed for reproducible science. This project's contribution to the Nation's data science workforce is broadened through the free and open distribution of its seminar materials and supplemental resources and its online instructor support community. To encourage adoption at Bay Area community colleges and universities, instructor training is provided through co-instruction and a teaching-the-teacher model. The project contributes to pedagogical research by identifying instructional approaches most effective in teaching data science to a diverse population of undergraduate students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.